9642 results about "Road surface" patented technology

The invention discloses an LED street lamp multi-scene intelligent regulation and control method and system for road illumination. The method comprises the steps that environment sensing parameters and equipment state parameters are collected; constructing a dynamic light scattering model according to the environment perception parameters and the equipment state parameters, predicting effective illumination distribution from the LED light source to the road surface in the current environment, and generating an LED spectrum compensation strategy; the real value of effective illumination distribution from the LED light source to the road surface is monitored in real time, the deviation between the real value and a predicted value is analyzed, and if the deviation exceeds a preset threshold value, the light attenuation coefficient, the road surface equivalent reflectivity and the LED working temperature are calculated in a reverse iteration mode according to the deviation so as to update the LED spectrum compensation strategy; and generating a PWM (Pulse Width Modulation) driving signal according to the updated LED spectrum compensation strategy, controlling the duty ratio of each sub light source in the LED array, and dynamically adjusting the LED spectrum distribution. According to the method, the LED operation parameters are dynamically regulated and controlled through the deviation between the true value and the predicted value of the effective illumination distribution, the light attenuation is compensated in real time, the spectral efficiency is optimized, and the LED power consumption is reduced on the premise of ensuring the illumination quality.

The invention discloses a pavement throwing detection method and device based on deep learning, electronic equipment and a program product. The method is realized through a trained detection model, the model adopts an LMSADet detection head, a multi-scale feature extraction and space attention mechanism is introduced into a task branch, and multi-scale modeling is decoupled from a backbone network and a neck network and integrated to the detection head so as to fit a detection task to directly optimize local details and scale differences of a throwing target. In order to suppress background interference and improve the recognition effect of fuzzy boundaries, the neck network is added into an MSHA module so as to efficiently capture the semantic relation between the thrown object and the background and enhance the regional understanding ability. A C3ESP module is introduced into the backbone network, deep features are extracted through stacking depth separable convolution, and information loss is avoided in combination with residual optimization fusion; meanwhile, a PEMA attention mechanism is introduced, the importance of different receptive field features is dynamically adjusted, the model focuses on key features, data information is captured more comprehensively, and therefore the detection performance is remarkably improved.

The invention relates to the technical field of automobile brake control, in particular to a self-adaptive brake kinetic energy recovery control method and system based on multi-sensor fusion. Tire wear and road surface feature data are collected through a multi-source sensing fusion unit, and a tire wear coefficient and a road surface friction coefficient are generated through an intelligent decision calculation unit; the method comprises the following steps of: integrating the nonlinear correlation of the multi-source sensing fusion unit and the self-adaptive control execution unit through a rule and data fusion algorithm, outputting a comprehensive friction coefficient, and dynamically adjusting the braking kinetic energy recovery force and response time by the self-adaptive control execution unit according to the comprehensive friction coefficient. The system comprises a multi-source sensing fusion unit, an intelligent decision calculation unit, the self-adaptive control execution unit and a closed-loop feedback calibration unit. The closed-loop unit corrects data deviation through cross validation of the laser radar and the motor torque inversion model, the problems of poor working condition adaptation and unreliable data are solved, and the kinetic energy recovery efficiency, the braking safety and the driving smoothness are improved.

The invention relates to an automobile seat adjusting method and device, a vehicle and a storage medium, and the method comprises the steps that multi-dimensional biomechanical data including body pressure distribution data of a driver and passengers and deep muscle group activity spectrum information are obtained; the method comprises the following steps: collecting motion state data and pavement environment data of an automobile, generating an automobile passenger environment coupling data set according to the motion state data and the pavement environment data of the automobile, and establishing a driving environment dynamic parameter library of the automobile according to the automobile passenger environment coupling data set; and based on the multi-dimensional biomechanical data and parameters in the driving environment dynamic parameter library, using a preset digital twin support model to determine control parameters of the automobile, generating an adjusting instruction of the automobile seat according to the control parameters, and adjusting the automobile seat according to the at least one seat adjusting action. Therefore, the problems that in the prior art, the intelligent degree and the individuation degree of seat adjustment are low, and continuous and comfortable sitting and riding experience is difficult to provide for a driver and passengers are solved.

The invention relates to the technical field of intelligent traffic infrastructure engineering, in particular to a roadbed settlement high polymer grouting repair grouting parameter optimization method, which comprises the following steps of: firstly, constructing a multi-source fusion training data set, and learning a prediction model based on a training machine; establishing a driving mapping relation between the grouting parameters and the road lifting effect and the maximum stress data of the repair area; then, based on the mapping relation, a multi-objective optimization model including pavement settlement repair precision, road stress, economic cost control and environmental friendliness evaluation is constructed; solving the model by adopting a Bayesian optimization algorithm to obtain an optimized grouting parameter combination; a marginal contribution of each parameter to a prediction result is calculated in combination with an SHAP interpretability analysis method, and an engineering decision basis is provided for parameter selection; constructing a transfer learning adaptation framework to adapt to different disease, geology and road types; and finally, establishing a real-time monitoring system, and realizing optimal control by combining sensor data and model feedback.

The invention belongs to the field of asphalt mixture performance analysis, and particularly discloses a fatigue performance analysis method for an asphalt mixture of an asphalt pavement, which comprises the following steps of: synchronously acquiring a dynamic strain field and a temperature field in a material by fusing an embedded sensor in a paving and rolling construction process; a temperature-strain coupled fatigue damage evolution model is combined to construct an on-line fatigue performance analysis system oriented to the whole construction process, so that a complex heat-force coupling environment and a non-uniform compaction process in actual paving operation are effectively restored; scale effect deviation and boundary condition distortion caused by difference between laboratory test piece preparation and on-site working conditions are remarkably reduced, and meanwhile, in the online detection process of the fatigue performance of the asphalt mixture, instant quantitative judgment of the performance state of the pavement structure is realized by positioning a damage remarkable overrun area; and the in-situ monitoring capability on the initial fatigue damage is fully reflected, so that the abnormal strain accumulation process before microcrack initiation can be captured.

The invention discloses a heavy traffic asphalt pavement damage evolution prediction system, and relates to the technical field of road engineering, and the system comprises a data acquisition module which is used for obtaining continuous multi-frame time sequence images of pavement cracks, a vehicle-mounted axle load spectrum and environment temperature and humidity data; the image processing and space-time modeling module is used for performing multi-scale fractal processing and dynamic time warping on the time sequence image and calculating a cross-scale fractal dimension sequence; the material performance association module is used for establishing and updating a probability association model of the fractal dimension and the shear modulus in real time according to the test data of the dynamic shear rheometer; the multi-physics field data fusion module is used for performing multi-physics field feature fusion through the fractal attention network to generate a multi-physics field collaborative damage correction factor; and the damage prediction and early warning module is used for predicting a fractal dimension growth trend through a fractal domain adaptive transfer learning algorithm, and outputting damage evolution early warning based on a viscoelasticity phase consistency verification result.

The invention relates to the technical field of intelligent identification, in particular to a highway pavement damage intelligent identification and evaluation method, which comprises the following steps: detecting a structure continuity and texture density region based on a pavement image, extracting crack, pit slot and track contour marks, grouping crack, pit slot and track texture to generate an identification graph, and evaluating the pavement damage. And extracting a crack area texture and gradient change to generate a difference layer, judging trend offset to generate an evolution label, and adjusting a classification boundary to evaluate a damage level. According to the method, the structure continuity and the texture density area are detected, the positioning precision is improved, misrecognition is reduced, direction gradient and texture changes are collected under multiple scales, the crack directions are clustered and grouped, the texture recognition stability is enhanced, the difference of the crack area and the periphery is analyzed, boundary jump is recognized, difference perception is enhanced, and the dynamic recognition depth is improved. Evaluation fuzzy errors are reduced, the overall process improves morphological representation precision and trend tracking and classification boundary stability, and the method adapts to various road condition changes.

The invention relates to the technical field of automobile electric control, in particular to a new energy automobile electric control steering control method and system, and the method comprises the steps: obtaining and preprocessing a four-wheel vibration data sequence, carrying out the segmentation and consistency evaluation of the vibration data sequence of each wheel, and selecting a current data sequence in an optimal segment to calculate a road condition influence coefficient. And matching the current data sequence with the historical vibration data sequence, calculating the similarity to determine the influence weight, adjusting the influence coefficient in combination with the steering angle change rate of the historical vibration data sequence, inputting the adjusted pavement condition influence coefficient into the ECU, dynamically adjusting the steering auxiliary torque, and realizing accurate steering control. According to the method, the optimal segment is selected through segment processing and consistency evaluation to accurately reflect the current road condition, the steering auxiliary torque is dynamically adjusted in combination with historical vibration data sequence matching and the steering angle change rate, and the driving safety and comfort are improved.

The invention discloses a vehicle instability active compensation method based on an angle module and related equipment, and the method comprises the steps: responding to a received steering instruction for a target vehicle, and determining a target yaw velocity and a target slip angle for the target vehicle according to the steering instruction; driving data and tire force constraint conditions of the target vehicle are obtained, and the tire force increment of the target wheel end of the target vehicle is determined according to the target yaw velocity, the target slip angle, the driving data and the tire force constraint conditions; wherein the tire force constraint condition is determined according to an adhesion coefficient estimation value of a road surface where the target vehicle is located; and controlling a target wheel end of the target vehicle to steer, brake or / and drive according to the tire force increment so as to correct a driving route of the target vehicle.

The invention discloses a curve driving collision early warning method and system based on multi-risk field fusion, and the method comprises the steps: 1, collecting vehicle dynamics data in real time, and carrying out the short-time prediction; step 2, geometric modeling and rasterization of a road; 3, constructing a geometric risk field; 4, constructing a kinetic energy risk field; 5, constructing a behavior risk field; step 6, constructing a comprehensive risk field; step 7, collision risk judgment; and step 8, generating an active intervention strategy. Collaborative perception and risk prediction of vehicles, roads and driving behaviors are realized through multi-risk field fusion, and potential collision hidden dangers of curve driving are identified in advance; a vehicle dynamics model and risk correction based on road adhesion are introduced to realize accurate risk assessment of the vehicle; multi-time window trajectory prediction and behavior modeling are adopted, so that the recognition capability of complex driving behaviors is improved; a closed-loop system from risk prediction and decision making to active control is realized, and the safety and stability of the vehicle driving in the curve scene are effectively improved.

The invention provides an automatic driving test scene generation method based on real traffic data, and solves the problems of low accident data utilization rate, SIL / HIL test splitting and insufficient boundary coverage in the prior art. Comprising the following steps: acquiring multi-source heterogeneous traffic accident data; cleaning data by adopting a joint interpolation-anomaly detection mechanism; vehicle dynamic sudden change characteristics within 0.5 second before braking are extracted through LSTM and DTW algorithms; constructing a three-dimensional scene pipeline driven by a physical engine, and dynamically associating the pavement slippery coefficient with the rainfall intensity; analyzing the accident text into simulation parameters by using a semantic-physical parameter converter; performing SIL-HIL cooperative verification: performing extreme illumination perception test and narrow road planning verification in an SIL environment, and realizing 1ms step length fault injection test in an HIL environment; positioning failure parameters based on Bayesian optimization; a GAN is adopted to generate a long-tail scene, and a test boundary is expanded by coupling extreme conditions such as rainstorm / low visibility; and outputting a standard scene library containing the collision probability thermodynamic diagram. The safety verification efficiency under the extreme working condition is remarkably improved.

The invention discloses a pavement disease image adaptive enhancement and restoration processing method, and belongs to the technical field of image processing, and the method comprises the steps: obtaining a pavement disease image, and carrying out the frequency domain transformation; recognizing a pavement material type based on the frequency domain features and determining a material sensing parameter set; performing multi-scale frequency domain decomposition according to the material perception parameter set, and performing adaptive enhancement on a low-frequency illumination component and a high-frequency detail component; carrying out disease feature detection and generating a disease perception weight map, and carrying out key enhancement on a disease area; according to the method, the self-adaptive enhancement model for road surface material perception is established, and targeted image enhancement processing is realized.

The invention relates to the technical field of deep learning and computer vision, and discloses a histogram interactive attention-based pavement crack segmentation network processing method and system, so as to enhance the edge detail fidelity and improve the crack segmentation precision. The method comprises the steps of image preprocessing, up-sampling, down-sampling, feature fusion and image reconstruction processing. Wherein global feature modeling in the intensity sub-boxes and among the sub-boxes is realized by constructing a histogram interactive attention module (HIA); a double-branch detail enhancement feedforward module (DDEF) is introduced to enhance spatial detail and high-frequency edge information expression; meanwhile, a Fourier jump enhancement module (FFSM) is adopted to jointly refine jump connection features in a spatial domain and a frequency domain. Through the synergistic effect of the modules, the network can realize continuous recovery and structural consistency modeling of a crack boundary in a complex pavement environment, so that the accuracy and the stability of a segmentation result are remarkably improved.

The invention discloses a self-adaptive constant-force vibration reduction mechanism base and a robot, and belongs to the technical field of specialized robots. Two sets of buffer units and vibration reduction units are arranged at the front end and the rear end of the base correspondingly; driving wheels are arranged on the buffer unit and the vibration reduction unit; the buffer unit comprises a horizontal buffer structure, a vertical buffer structure and a support; the horizontal buffer structure is slidably connected with the support in the vertical direction; the top of the vertical buffer structure is hinged to the support, and the bottom is fixedly connected with the driving wheel; the support is fixedly connected with the base; the vibration reduction unit comprises a rotating support and a constant force component. According to the self-adaptive constant-force vibration reduction mechanism base and the robot, the problems that in the prior art, a vibration reduction mechanism cannot completely avoid vibration generated by a wheel type moving device in the vertical direction when the wheel type moving device moves on an uneven road surface, and consequently bolt looseness, wire harness interface looseness, material fatigue strain and the like occur can be effectively solved; and therefore, the wheel type moving device has a fault in the operation process and cannot stably operate, and the like.

The invention discloses a multi-modal dynamic compensation road disease intelligent detection and risk assessment system, and relates to the technical field of artificial intelligence and computer vision, and the system comprises an image collection module which is used for obtaining a road surface image in real time through a camera device, and transmitting the image to a preprocessing module; the preprocessing module is electrically connected with the image acquisition module and is used for carrying out graying, noise reduction, contrast enhancement and geometric correction operation on the image and outputting a standardized image; the feature extraction module is electrically connected with the preprocessing module. According to the road disease detection system provided by the invention, by integrating a plurality of modules, high efficiency and intelligence of road disease detection are realized, compared with traditional manual inspection, the system not only improves the detection efficiency, but also remarkably enhances the objectivity and accuracy of detection, and is particularly suitable for real-time monitoring requirements of a large-scale road network; the image acquisition quality is effectively improved, and the effectiveness of feature extraction can be ensured.

The invention discloses a pavement maintenance decision-making method, system and equipment, a medium and a product, and relates to the field of highway engineering management. The method comprises the following steps: firstly, collecting performance data of a target road section, and identifying a to-be-optimized pavement maintenance unit through a threshold judgment method or a K-means clustering algorithm; encoding each maintenance measure type and the corresponding maintenance opportunity into a real number vector, and taking the real number vector as a maintenance scheme code; constructing a multi-target fitness function covering pavement performance, maintenance cost and carbon emission; carrying out iterative optimization on the maintenance scheme through a particle swarm optimization algorithm on the basis, and outputting a particle swarm optimization solution set; performing rapid non-dominated sorting and congestion degree distance calculation on the particle swarm optimization solution set, and extracting a Pareto optimal solution set; and fusing the Pareto optimal solution and the full-life-cycle comparison data of the target road section, and outputting a maintenance decision scheme for each pavement maintenance unit, so that the decision efficiency and the scientificity, accuracy, sustainability and refinement degree of the maintenance decision scheme are improved.

The invention relates to the technical field of cooperative control, in particular to a cooperative control method for pavement construction machinery, which comprises the following steps: acquiring a control instruction and extracting a rhythm interference number, marking a path coincidence conflict section, constructing an alternative path node sequence, and recombining an execution sequence to realize cooperative control. According to the method, by extracting interference numbers of rhythm fluctuation of control instructions and comparing the interference numbers with node position information, classification and positioning of construction paragraphs with abnormal rhythm are realized, and rhythm label reconstruction is carried out according to rhythm change of a control chain in combination with classification identification of path overlapping areas to form new path sequence identification; optimized adjustment of control instruction rhythm distribution and coordinated rearrangement of a path instruction sequence are completed, so that the operation states of multiple construction machines in a path crossing area tend to be balanced, the path distribution definition and rhythm consistency are improved, control interference and task delay caused by frequent path conflicts are avoided, and the construction efficiency is improved. And the path continuity and the rhythm orderliness are kept consistent in the operation process.

The embodiment of the invention provides a road disease inspection method and device based on AI recognition, equipment and a storage medium, and is used for road maintenance. The method comprises the steps of performing multi-source data synchronous acquisition on a road surface to obtain an original image-positioning data set, performing dynamic interference suppression and image enhancement on the original image-positioning data set to obtain a stable enhanced image sequence, and performing disease target detection and classification in combination with a deep learning recognition model to obtain a disease target set, and performing multi-target space-time correlation and satellite positioning data fusion on the disease target set to obtain a stable target trajectory set, performing geometric coordinate conversion on the stable target trajectory set to obtain a road disease data set, and performing clustering analysis through a spatial clustering analysis algorithm to generate a road disease maintenance strategy report. Through the technical means of multi-source cooperation, deep learning, space-time fusion and the like, high-precision and intelligent detection of road diseases is realized, the road maintenance efficiency is improved, and the manpower and material resource cost is reduced.

The invention discloses a semi-rigid asphalt pavement crack depth estimation method, equipment and a medium, and relates to the technical field of road detection. The method comprises the steps of collecting a high-resolution image of a road surface through a mobile detection platform, segmenting a crack region in the high-resolution image by adopting an image segmentation model, and obtaining a binary image of a crack; pixel accumulation, edge detection, skeletonization, skeleton trimming and orthogonal projection are carried out on the crack binary image to extract surface parameters of the crack, and the surface parameters comprise the type, the length, the width and the area; inputting the fracture surface parameters into the fracture depth prediction model, and outputting a fracture depth prediction value; and generating a crack depth grade label according to the depth predicted value, and outputting a corresponding maintenance strategy. According to the method, the crack surface parameters and the pavement structure parameters are combined, the prediction result has good accuracy and consistency through the coupling relation with the depth index, and the method adapts to different crack types and working conditions.

The invention belongs to the technical field of asphalt pavement repairing, and particularly relates to a local repairing technology of an asphalt pavement, which comprises the following specific contents: detecting a disease range by adopting ground three-dimensional laser scanning or ground penetrating radar, determining a repairing boundary, sampling and detecting the aging degree, the moisture content and the grading type of the asphalt of the original pavement, and determining the repairing quality of the asphalt pavement. According to the method, the bonding strength of the repairing material and the asphalt layer of the original pavement is enhanced by heating and activating the interface of the original pavement and emulsifying the asphalt bonding layer, and matching the repairing material with the shrinkage coefficient of the original pavement and reinforcing and compacting the edge, so that the condition that the repaired position is cracked, loosened and damaged in a short time is avoided; warm-mixed asphalt is adopted, the heating temperature is reduced by 40-60 DEG C, cold-mixed emulsified asphalt is popularized, and the asphalt does not need to be heated, so that release of a large amount of volatile organic compounds (VOCs), polycyclic aromatic hydrocarbons (PAHs) and dust is effectively avoided, and air pollution is avoided.

The invention discloses an intelligent self-adaptive vehicle suspension system based on deep learning and a control method, and belongs to the technical field of vehicle suspensions.The system comprises a quantum state collaborative sensing module, a Lie group road condition prediction module, a topology invariant optimization module and a dual closed-loop control module; the quantum state collaborative perception module realizes multi-sensor deep fusion based on a quantum state entanglement theory, the fusion precision is improved by more than 40%, the Lie group road condition prediction module establishes a road surface dynamic evolution model by using a special Euclidean group SE (3), the prediction precision is improved by 45%, and the topology invariant optimization module extracts road surface topology invariants through continuous coherence, so that the road surface dynamic evolution model is optimized. The suspension parameters are optimized on the octyl manifold, the convergence speed is increased by 60%, the dual closed-loop control module constructs a forward prediction and backward correction double closed loop, the response time is shortened to 15 milliseconds, and the comfort and controllability of the vehicle are remarkably improved.

The invention provides a highway pavement structure settlement deformation monitoring and identification method and system based on digital twinning, and belongs to the field of fiber bragg grating sensing structure health monitoring. According to the method, the vibration mode characteristics of the pavement structure are obtained through the vibration pickup, the modal information is calculated by matching numerical values, and the pavement deformation is reconstructed by adopting a modal vibration mode matrix and monitoring strain data; sensor layout optimization is carried out based on an actual test road section, theoretical calculation deviation existing under the road surface strong constraint condition is corrected by means of a neural network model, and the road surface deformation type disease development trend is predicted. The method can effectively monitor pavement structure settlement evolution development under unfavorable geological conditions, early warns sudden collapse risks in advance, and provides an important basis for intelligent monitoring of road settlement. A digital model is built based on the multi-source data, and real-time diagnosis of the state of the monitored road section and early warning and treatment of settlement risks are achieved.

The invention discloses an ultra-high-speed three-dimensional laser scanning pavement skid resistance evaluation method and system, and the method comprises the steps: scanning a target pavement through a three-dimensional laser scanner, obtaining the three-dimensional point cloud original data of the pavement, carrying out the preprocessing of the three-dimensional point cloud original data of the pavement, and carrying out the calculation of the three-dimensional point cloud original data of the pavement; constructing a road surface three-dimensional texture model according to the preprocessed road surface three-dimensional point cloud data, constructing a reference surface of a target road surface, projecting the reference surface into the road surface three-dimensional texture model, extracting a plurality of feature parameters related to the anti-skid performance of the road surface based on the projected road surface three-dimensional texture model, and calculating the anti-skid performance of the road surface according to the extracted feature parameters. According to the method, the weights of the extracted multiple characteristic parameters related to the anti-skid performance of the road surface are determined, the multiple characteristic parameters are subjected to weighting processing, and the environment penalty factor is introduced to correct the weighting processing result to obtain the anti-skid performance index, so that the anti-skid performance of the target road surface is evaluated by using the anti-skid performance index, and the evaluation accuracy of the anti-skid performance of the target road surface is improved. Therefore, the evaluation accuracy and evaluation efficiency of the anti-skid performance of the road surface are improved.

The invention discloses a roadbed and pavement compactness detection and regulation method for road engineering. The method comprises the following steps: deploying an intrusive sensor array to monitor an internal physical state and water-heat-force coupling phase change data; synchronously acquiring operation data of intelligent compaction equipment and scanning data of nondestructive detection equipment, and identifying a phase change critical point of the material; a disturbance propagation coefficient is calculated through a mechanical transmission chain model after data fusion, a target point is determined, and automatic micro sampling is carried out; and fusing all data to establish a macro-medium-micro three-scale fused compaction prediction model, generating a real-time regulation and control instruction to realize closed-loop control, and finally generating a three-dimensional compaction degree distribution digital twin model. According to the method, multi-source data are integrated, material phase change and mechanical transfer rules are associated, and the stability and the intelligent level of compaction quality are improved.

The invention discloses a tunnel pavement concrete early-age strength prediction method based on multi-network fusion modeling. The method comprises the following steps: S1, continuously and equally dividing a tunnel center line according to a certain interval; s2, only keeping a left-right adjacent segment relation for the segment, and calculating a direction weighted thermal gradient, a direction weighted humidity gradient and a direction weighted traffic disturbance gradient based on the state vector; s3, writing the state vector of each segment into a sliding window with a set time length at a fixed sampling period; s4, inputting the dynamic graph input tensor into a space-time diagram neural network; s5, comparing the prediction sequence with a preset strength threshold value, identifying potential weak segments, and calculating risk confidence in combination with the gating coefficient; and S6, writing the maintenance instruction packet into a corresponding PLC channel. Potential weak sections can be recognized, spraying, heat preservation curtains and ventilation equipment can be linked, and local precise maintenance and batch open traffic unified management are achieved.

The invention relates to a pavement disease detection method, system and device based on a hybrid architecture, and a storage medium. The method comprises the following steps: obtaining pavement image data; the method comprises the steps that data are input into a re-parameterized feature extraction network, the network is based on an HGNetV2 architecture, a RCHGBlock module is formed by embedding a RepConv structure into HGBlock, and a plurality of modules are cascaded and stacked to construct a four-stage progressive feature pyramid structure; based on a high-level semantic layer of a feature pyramid, integrating a space edge perception enhanced attention mechanism, enhancing disease edge features through a dual-path complementary processing framework of an edge extraction path and a standard convolution path, and performing multi-scale feature fusion by using an improved C3K2-SEAM module as a feature fusion unit; and performing end-to-end disease detection based on the fusion features, and outputting disease types, positions and confidence information. Compared with the prior art, the method has the advantage that the disease detection precision and robustness in a complex scene are remarkably improved.

The invention relates to a tunnel portal road ice condensation model generation method based on artificial intelligence. According to the method, a multi-dimensional sensor network is deployed at a tunnel portal to collect environment and road surface data in real time, a characteristic variable set of quantitative humidity trap effects such as temperature gradient and humidity gradient is constructed, and then a dynamically weighted environment association graph structure is established according to the spatial position of a sensor; a graph neural network model is trained by using the graph structure to capture the spatial conduction rule and time evolution characteristics of microclimate parameters, and finally probability distribution output by the model is converted into a road freezing model, so that accurate positioning of a tunnel portal freezing high-risk area is realized, the spatial blind area of a traditional uniform distribution model is overcome, and the construction efficiency is improved. The early warning reliability is obviously improved, and a scientific basis is provided for accurate scheduling of ice melting resources.

The invention provides a vehicle driving state estimation method and system in a centralized driving mode, and relates to the technical field of electric automobiles, and the method comprises the following steps: determining wheel dynamic parameters based on sensor detection information and a seven-degree-of-freedom dynamic model related to wheels, and combining a preset Dugoff tire model to obtain initial tire force parameters; a vehicle-mounted infrared camera is used for obtaining road surface image information, the road surface image information is analyzed through a deep convolutional neural network based on semantic segmentation and a vehicle dynamics inversion method, an adaptive fusion mechanism is introduced for adjustment, and a target road surface adhesion coefficient is determined; correcting the initial tire force parameter by using the target road adhesion coefficient to obtain a target tire force parameter; inputting the current sensor detection information and the target tire force parameter into a self-adaptive unscented Kalman filter to obtain vehicle driving state information; the vehicle driving state information comprises the longitudinal speed, the transverse speed, the yaw velocity and the side slip angle of each tire.

The invention provides a semi-active suspension vibration control method and system based on preview fusion and game decision, and the method comprises the following steps: obtaining pavement preview information through a forward sensor, carrying out the signal processing, and generating a future pavement preview sequence in real time; establishing a two-degree-of-freedom 1 / 4 whole vehicle vertical dynamical model; under a model prediction control framework, participants of a non-cooperative Nash game model are defined, a corresponding cost function is designed, a damping coefficient is discretized by adopting mixed integer programming, Nash equilibrium is solved by adopting a branch and bound method, and an optimal damping coefficient is obtained; mapping the optimal damping coefficient into optimal damping force meeting constraint conditions; hysteresis force compensation is conducted through a hysteresis model, and target control current is obtained through damping force obtained after hysteresis compensation according to the mapping relation between the damping force of the magnetorheological damper and the control current; and outputting the target damping force according to the instruction of the target control current. According to the invention, autonomous balance adjustment of multiple control targets is realized.